Artificial Intelligence: Introduction to application scenario knowledge of artificial neural networks

Today I will talk to you about the application scenarios of artificial neural networks in several fields.

1. Application in the field of information

1.1 Information processing

Artificial neural networks can imitate or replace functions related to human thinking to achieve problem solving , automatic problem diagnosis, thereby solving problems that cannot or are difficult to solve with traditional methods,

Scenarios: intelligent instruments, automatic tracking and monitoring instruments, automatic alarm systems, automatic fault diagnosis systems, etc.

1.2 Pattern recognition

Pattern recognition is mainly the processing and analysis of various forms of information about things or phenomena, so that it can describe and identify things or phenomena. , the process of classification and explanation.

Pattern recognition mainly includes statistical pattern recognition and structural pattern recognition methods, among which artificial neural network is a common method for pattern recognition.

Scenarios: speech recognition, image and text recognition, fingerprint recognition, face recognition, handwritten character recognition, etc.

2. Applications in the transportation field

Transportation problems are highly nonlinear and the data are massive and complex, so they are very suitable for processing by artificial neural networks.

Scenario: Very good results have been achieved in the fields of car driver behavior simulation, road maintenance, vehicle detection and classification, traffic flow prediction, subway operation and traffic control.

3. Application in the economic field

3.1 Market commodity price prediction

The prediction of commodity prices will be subject to the analysis of many factors such as market supply and demand. Traditional Due to the inherent limitations of statistical economics methods, it is difficult to make more accurate predictions of price changes. Artificial neural networks can be used to establish more reliable predictions based on per capita income, family size, loan rates, urban consumption levels, etc. The model can achieve a more scientific prediction of commodity prices.

Scenario: Forecasting market commodity prices

3.2 Risk assessment

Risk assessment is a type of investment activity that exists A preventive measure to prevent uncertainty and thereby cause economic losses. The use of artificial neural networks can provide a more reasonable credit risk model based on actual risk sources, and calculate the risk evaluation coefficient to provide a more reasonable solution for actual risk investment.

Scenarios: credit card processing, purchasing financial products, stocks, etc.

4. Application in the medical field

4.1 Detection and detection of biological signals Automatic analysis

Currently, most medical testing equipment outputs data in the form of continuous waveforms. These waveform data are the basis for medical diagnosis. Artificial neural network is an adaptive dynamic system connected by a large number of simple processing units. It has functions such as massive parallelism, distributed storage, and adaptive learning. It can be used to solve problems that are difficult to solve with conventional methods in biological signal analysis and processing. question.

Scenarios: EEG signal analysis, EMG and gastrointestinal signal recognition, ECG signal compression, medical image recognition and processing, etc.

4.2 Medical expert system

The traditional expert system stores the existing experience and knowledge of experts in the computer with fixed rules to establish a knowledge base, and then uses logic A reasoning approach to medical diagnosis. The traditional method has bottlenecks in the way of acquiring knowledge, and the increase in database size has caused a knowledge explosion, so the work efficiency is relatively low. Artificial neural networks provide better prospects for medical expert systems based on nonlinear parallel processing.

Scenario: Research in the fields of anesthesia and critical care medicine involves the analysis and prediction of physiological variables. For example, there are relationships and phenomena that have not been discovered or have no definite evidence in clinical data, signal processing, automatic discrimination and detection of interference signals, prediction of various clinical conditions, etc.

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